Building Recommender Systems with Machine Learning and AI from Coursera

This course teaches you to use Python, AI, machine learning, and deep learning to build recommender systems, from simple engines to hybrid ensemble recommenders. You'll start with an introduction to recommender systems and Python, evaluate systems, and explore the recommender engine framework.

You'll learn content-based recommendations, neighborhood-based collaborative filtering, and methods like matrix factorization and SVD. The course covers applying deep learning and AI to recommendations, scaling datasets with Apache Spark, solving real-world challenges, and studying systems like YouTube and Netflix. By the end, you'll build recommendation systems to help users discover new products and content.

You'll test and evaluate algorithms with Python, use K-Nearest-Neighbors, address large-scale issues, make session-based recommendations with neural networks, and compute recommendations with Apache Spark. This course is for developers with basic Python knowledge.

What's inside

Syllabus

Getting Started

In this module, we will lay the foundation for the course by setting up the development environment with Anaconda, familiarizing you with the course materials, and introducing you to creating simple movie recommendations.

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Uses Python, AI, machine learning, and deep learning, which are all highly relevant technologies in the field of recommender systems and data science

Covers content-based recommendations, neighborhood-based collaborative filtering, matrix factorization, and SVD, which are standard techniques used in building recommender systems

Explores scaling datasets with Apache Spark, which is essential for handling large-scale data in real-world recommender systems

Includes case studies of YouTube and Netflix, which offer insights into how large companies approach recommendation strategies

Features an optional module on deep learning, which may require additional study for those without prior experience in neural networks

Requires Anaconda, TensorFlow, and Keras, which may require learners to install additional software and libraries

Reviews summary

Practical recommender systems with ml/ai

According to learners, this course offers a practical approach to building recommender systems, covering various algorithms from collaborative filtering to deep learning, including scaling with Apache Spark. Many appreciate the hands-on projects that help solidify understanding. However, a significant number of students note that the course requires a strong prerequisite knowledge in Python and machine learning fundamentals and may not be suitable for absolute beginners. Some older reviews mentioned issues with code examples, though recent feedback suggests these problems may have been addressed.

Some report issues; seems to be improving.

"But some of the code examples provided had issues and needed significant debugging..."

"Couldn't get the code working in the early modules. Felt like the environment setup instructions or dependencies were missing/wrong."

"Code for that module also seemed less polished."

"The practical exercises are good, though some require significant effort to get running correctly."

Explores various fundamental algorithms.

"Covers a wide range of recommender techniques."

"Finally, a course that dives deep into both traditional and deep learning methods for recommenders."

"The coverage of different algorithms and how to evaluate them was comprehensive."

"Decent course. Covers the key algorithms."

Hands-on activities build practical skills.

"The practical projects were incredibly valuable and really helped solidify my understanding."

"The hands-on approach to building recommenders from scratch was exactly what I needed."

"The projects were tough but rewarding and gave me hands-on experience."

"Learned how to use practical tools and strategies that I could apply immediately to my work"

Some topics could be more in-depth.

"Some math concepts could use more detailed explanation."

"the deep learning section felt a bit rushed compared to other parts. I was hoping for more examples or depth there."

"Useful concepts, but felt the math behind some algorithms wasn't fully explained."

Requires strong ML/Python background.

"it assumes a pretty solid background in Python and general ML concepts. If you're not comfortable with those, you might struggle."

"Found this course very challenging. As someone relatively new to ML, I felt lost quickly."

"The course description mentioned Python basics, but you definitely need more than basics. Struggled with the math and ML theory..."

"I felt the pace was fast, and prerequisite knowledge wasn't clearly reinforced."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Building Recommender Systems with Machine Learning and AI with these activities:

Review Linear Algebra Fundamentals

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Reinforce your understanding of linear algebra concepts like matrix operations and dimensionality reduction, which are crucial for understanding matrix factorization methods used in recommender systems.

Browse courses on Singular Value Decomposition

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Review matrix operations such as multiplication and inversion.
Study eigenvalue and eigenvector concepts.
Practice solving linear systems of equations.

Brush Up on Python Data Structures

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Strengthen your Python skills, particularly with data structures like lists, dictionaries, and Pandas DataFrames, which are essential for manipulating and processing data in recommender systems.

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Practice creating and manipulating lists and dictionaries.
Review Pandas DataFrame operations.
Work through basic NumPy array exercises.

Read 'Programming Collective Intelligence'

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Gain a deeper understanding of collaborative filtering techniques by studying a classic text on the subject.

View Programming the Semantic Web: Build Flexible... on Amazon

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Read the chapters on collaborative filtering and item-based recommendations.
Implement the examples in Python.
Compare the book's approach to the course material.

Five other activities

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Implement KNN from Scratch

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Solidify your understanding of K-Nearest Neighbors (KNN) by implementing the algorithm from scratch using Python and NumPy. This will help you understand the underlying mechanics of the algorithm and its application in content-based filtering.

Browse courses on K-Nearest Neighbors

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Write a function to calculate distance between two data points.
Implement the KNN algorithm to find the nearest neighbors.
Test your implementation on a sample dataset.

Build a Simple Movie Recommender

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Apply the concepts learned in the course by building a simple movie recommender system using either content-based or collaborative filtering techniques. This hands-on project will reinforce your understanding of the algorithms and evaluation metrics.

Browse courses on Content-Based Filtering

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Choose a dataset of movies and user ratings.
Implement a content-based or collaborative filtering algorithm.
Evaluate the performance of your recommender system.

Read 'Deep Learning with Python'

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Expand your knowledge of deep learning techniques relevant to recommender systems by studying a comprehensive guide.

View Deep Learning with Python, Second Edition on Amazon

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Read the chapters on neural networks and autoencoders.
Implement the examples using Keras.
Explore how these techniques can be applied to recommender systems.

Write a Blog Post on Recommender Systems

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Solidify your understanding of recommender systems by writing a blog post explaining different algorithms, evaluation metrics, and real-world applications. This will help you articulate your knowledge and share it with others.

Browse courses on Recommender Systems

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Choose a specific topic within recommender systems.
Research and gather information on the topic.
Write a clear and concise blog post explaining the topic.

Contribute to an Open Source Recommender Project

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Deepen your understanding of recommender systems by contributing to an open-source project. This will give you practical experience working with real-world code and collaborating with other developers.

Browse courses on Open Source

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Find an open-source recommender system project on GitHub.
Identify a bug or feature to work on.
Submit a pull request with your changes.

Career center

Learners who complete Building Recommender Systems with Machine Learning and AI will develop knowledge and skills that may be useful to these careers:

Recommender Systems Specialist

A recommender systems specialist focuses on the development and optimization of recommendation algorithms, and this course is a comprehensive training program in this specialty. The course provides a thorough grounding in content-based filtering, collaborative filtering, matrix factorization, and deep learning approaches for recommender systems. It also covers large-scale data processing using Apache Spark. This course helps provide the specific expertise required for success in this role. The case studies on Netflix and Youtube system designs will also be helpful in understanding industry practices.

See salaries and explore the career path for Recommender Systems Specialist

Algorithm Developer

An algorithm developer designs and implements algorithms for various purposes, and this course provides crucial training relevant to this role by teaching the design of recommendation algorithms. This includes content-based, collaborative filtering, and matrix factorization methods, all covered in the course. The course material on evaluating algorithms enhances the developer's ability to create effective systems. Because this course goes over a number of different architectures for recommender systems, it gives the algorithm developer a number of relevant, immediately useful ideas.

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Artificial Intelligence Engineer

An artificial intelligence engineer develops and implements AI solutions, and this course helps build expertise in AI by teaching the creation of recommender systems, a common AI application. The course covers essential AI concepts, such as deep learning, neural networks and collaborative filtering. This course also delves into the practical application of AI in recommender systems, and by the end, the learner will gain proficiency in using AI to help users discover new products and content. The material on using deep learning for recommendations will be especially useful for AI engineers.

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Machine Learning Engineer

A machine learning engineer builds and deploys machine learning models, and this course helps build a foundation in creating recommender systems, which are a common machine learning application. Specifically, this course covers several machine learning techniques such as content-based filtering, collaborative filtering, and matrix factorization. The course also introduces deep learning approaches, and practical methods for scaling datasets, a crucial skill for any machine learning engineer. This course may be particularly useful as it will help one create a portfolio project to show prospective employers using real world data.

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Applied Scientist

An applied scientist uses scientific methods to address practical problems, and this course helps build the relevant skills to tackle practical engineering problems. An applied scientist might see this course as useful because it goes over methods for creating recommendation systems including content filtering and collaborative filtering. Because this course also addresses use cases such as video recommendations the applied scientist can use the material on this course to gain experience useful for a number of different projects. Additionally, material on scaling datasets will be particularly helpful for scientists working with large scale applications.

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Data Scientist

A data scientist uses data analysis and modeling to solve business problems, and this course helps develop data science skills that are directly applicable to building recommender systems. The course explores various machine learning techniques, including content-based filtering and matrix factorization. It also dives into model evaluation, ensuring you build models of high quality. Data scientists will find the material on using Apache Spark, and scaling datasets, to be practical for working with large amounts of data. The course provides hands-on experience with Python, essential for data science work.

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Software Developer

A software developer builds software applications, and this course helps one gain expertise in specific algorithms that they can implement in their own products. A software developer might find this course helpful because it goes over methods for creating recommendation systems including content filtering and collaborative filtering. Because this course uses Python, a widely used language, developers can apply skills they learn here to a number of different projects. Additionally, the course material on scaling datasets will be particularly helpful for developers of large scale applications.

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Data Engineer

A data engineer builds and maintains data infrastructure, and this course helps build an understanding of the machine learning algorithms that operate on that infrastructure. This course can be helpful to a data engineer working with data that is used to feed recommender systems. The course's material on scaling datasets with Apache Spark, and working with large-scale data processing will be very helpful to a data engineer. The data engineer will better understand the needs of the machine learning systems they support.

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Research Scientist

A research scientist conducts research and experiments, and this course may be helpful for a scientist interested in building recommenders. The course covers various algorithms, like matrix factorization and collaborative filtering, which are all relevant to various scientific research projects. The course also studies large scaling problems that will be helpful for research with large datasets. The research scientist will find the course provides a good foundation for research into recommender systems and machine learning more broadly.

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Data Analyst

A data analyst explores and interprets data to provide insights, and this course provides a relevant skill set for analyzing user behavior for recommendations. While a data analyst may not build the models themselves, understanding the underlying algorithms, like collaborative filtering, helps build valuable insight. The course's focus on evaluating recommender systems is particularly useful for understanding how to measure the impact of recommendations. The course is not a direct application of a data analyst's work, but the knowledge gained may be helpful to a data analyst working with recommender system data.

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Product Manager

A product manager oversees the development and success of a product, and this course helps them understand the technology behind a crucial feature of many modern products. Although a product manager may not implement code directly, having an understanding of how a recommender system works is extremely helpful in making decisions about the product. The course provides a good overview of different algorithms and their trade-offs, such as content-based filtering and deep learning methods, and this will be useful for a product manager overseeing features that implement these technologies. This course directly provides useful insight to the product manager.

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Business Intelligence Analyst

A business intelligence analyst analyzes data to inform business decisions, and this course may be helpful for analysts working with data that involves recommendations. While the role may not always implement recommender systems, understanding these systems, and how they work is crucial to understanding user behavior, especially in e-commerce. The course's focus on evaluating recommender systems is useful for understanding the impact of recommendations. The course material on real-world challenges also provides valuable context for understanding the use of recommendation systems in business. The course will be helpful in providing context for the data they analyze.

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Analytics Consultant

An analytics consultant provides data-driven solutions for different organizations, and this course can help them understand a key technology behind personalized services. Recommender systems are crucial to many modern businesses. The course content on content-based and collaborative filtering techniques helps the consultant understand how these systems work and the kind of data they require. This provides a foundation to evaluate the effectiveness of recommender systems in a business context. The course is not a direct fit, but can contribute useful knowledge.

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Information Architect

An information architect designs and organizes information structures. This individual may work on the structure of a system that implements a recommendation engine. The course might be helpful for an individual who is looking to understand what the needs are of a modern recommendation system, such as how recommender engines are designed and what algorithms they utilize. The course content on different algorithms and frameworks will be useful for this kind of role, though the course is not a direct fit for most information architects.

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Computational Linguist

A computational linguist develops algorithms and models for natural language processing, and this course can be helpful as it provides insights into recommendation systems, which can incorporate NLP. While this course does not focus on natural language processing, its material on content-based recommendations and collaborative filtering, can be applied to recommend text. Specifically, content-based filtering can help select articles or documents based on content similarity. This course may be useful for building recommendation capabilities in NLP products.

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Building Recommender Systems with Machine Learning and AI

What's inside

Syllabus

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Reviews summary

Practical recommender systems with ml/ai

Activities

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